Extra write scaling for performance and reliability

ABSTRACT

A computing device includes an interface configured to interface and communicate with a dispersed storage network (DSN), a memory that stores operational instructions, and a processing module operably coupled to the interface and memory such that the processing module, when operable within the computing device based on the operational instructions, is configured to perform various operations. For example, the computing device monitors storage unit (SU)-based write transfer rates and SU-based write failure rates associated with each of the SUs for a write request of encoded data slices (EDSs) to the SUs within the DSN. The computing device generates and maintains a SU write performance distribution based on monitoring of the SU-based write transfer rates and the SU-based write failure rates and adaptively adjusts a trimmed write threshold number of EDSs and/or a target width of EDSs for write requests of sets of EDSs to the SUs within the DSN.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility patent application claims priority pursuant to35 U.S.C. §119(e) to U.S. Provisional Application No. 62/211,975,entitled “STORING ENCODED DATA SLICES IN A DISPERSED STORAGE NETWORK,”filed Aug. 31, 2015, which is hereby incorporated herein by reference inits entirety and made part of the present U.S. Utility patentapplication for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION

Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

Prior art data storage systems do not provide adequate means to handlevariability of performance, latency, etc. while providing effectiveservicing to users thereof. For example, the overall network operationof a data storage system may change over time (both positively andnegatively). Also, such a data storage system may experience failuresthat degrades the performance thereof. The prior art does not provideadequate means to maintain a high level of performance while combattingsuch effects.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of another embodiment of a dispersedstorage network (DSN) in accordance with the present invention;

FIG. 10A is a diagram illustrating an embodiment of a method forexecution by one or more computing devices in accordance with thepresent invention;

FIG. 10B is a diagram illustrating another embodiment of a method forexecution by one or more computing devices in accordance with thepresent invention;

FIG. 11A is a schematic block diagram of an example of an acceptable SUwrite performance distribution relative to determined characteristicsassociated with operation of a DSN in accordance with the presentinvention; and

FIG. 11B is a schematic block diagram of an example of variousparameters associated with a set of encoded data slices (EDSs) storedwithin storage units (SUs) in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data on behalf of computing device 14. With the use ofdispersed storage error encoding and decoding, the DSN 10 is tolerant ofa significant number of storage unit failures (the number of failures isbased on parameters of the dispersed storage error encoding function)without loss of data and without the need for a redundant or backupcopies of the data. Further, the DSN 10 stores data for an indefiniteperiod of time without data loss and in a secure manner (e.g., thesystem is very resistant to unauthorized attempts at accessing thedata).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN module 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an 10 interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the 10 deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as 10 ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 60 is shown inFIG. 6. As shown, the slice name (SN) 60 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a schematic block diagram of another embodiment 900 of adispersed storage network (DSN) in accordance with the presentinvention. This diagram provides a is a schematic block diagram ofanother embodiment of a dispersed storage network (DSN) that includesthe DSN processing unit (or computing device) 16 of FIG. 1, the network24 of FIG. 1, and a storage set. The DSN processing unit (or computingdevice) 16 includes the DS client module 34 of FIG. 1. The DS clientmodule 34 includes one or more outbound DS processing 1-x, where eachoutbound DS processing may be implemented utilizing the outbound DSprocessing 80 of FIG. 3. The storage set includes a set of DS execution(EX) units 1-WT, e1-eE, where WT=write threshold number. Each DSexecution unit may be implemented utilizing the DS execution unit 36 ofFIG. 1. Hereafter, each DS execution unit may be interchangeablyreferred to as a storage unit and the storage set may be interchangeablyreferred to as a set of storage units. The DSN functions to store datain the storage set.

In an example of operation of the storing of the data the DS clientmodule 34 determines a number of extra slices E storage in the storageset when storing a data segment, where data is divided into a pluralityof data segments that includes the data segment, where each data segmentis dispersed storage error encoded in accordance with dispersalparameters to produce a set of encoded data slices, and where a writethreshold number of encoded data slices plus E number of extra encodeddata slices of the set of encoded data slices are stored in the storageset. The determining includes one or more of generating E based on oneor more of a previous value of E, a historical performance level ofaccessing an encoded data slices via the network 24 to the set ofstorage units (e.g., determine performance of each outbound DSprocessing where each outbound DS processing provides a performancelevel indicator, interpret timing of receiving a write slice response),a valid range of E (e.g., 0 to n-WT), a desired level of performance(e.g., minimizing access latency), and a desired level of resourceutilization (e.g., lowering network bandwidth utilization, loweringprocessing power usage, etc.). For example, the DS client module 34determines to raise E to lower access latency while raising resourceutilization. As another example, the DS client module 34 determines tolower E to lower resource utilization while raising access latency.

Having determined E, the DS client module 34 facilitates storage of awrite threshold number of encoded data slices of the set of encoded dataslices. For example, the DS client module 34 issues, via the network 24,write slice requests 1-WT to DS execution units 1-WT, where the writeslice requests include encoded data slices 1-WT. When the number ofextra encoded data slices E is further than zero, the DS client module34 facilitates storage of the number of extra encoded data slices instorage units of the storage set. For example, the DS client module 34issues, via the network 24, extra write slice requests 1-E to the DSexecution units e1-eE, where the extra write slice requests includeencoded data slices x1-xE (e.g., from remaining slices of n-WT, wheren=an information dispersal algorithm width of the dispersal parameters.

In an example, the DS processing unit (or computing device) 16 is incommunication with a storage set 910 via network 24. The DS processingunit (or computing device) 16 includes an interface configured tointerface and communicate with a dispersed storage network (DSN) thatincludes the storage set 910 (e.g., that may include a number of SUs),memory that stores operational instructions, and a processing moduleoperably coupled to the interface and to the memory, wherein theprocessing module, when operable within the computing device based onthe operational instructions, is configured to perform variousfunctions.

In an example of operation and implementation, the DS processing unit(or computing device) 16 monitors storage unit (SU)-based write transferrates and SU-based write failure rates associated respectively with eachof a storage units (SUs) (e.g., of storage set 910) for a write requestof a set of encoded data slices (EDSs) to the SUs within the DSN. Notethat a data object is segmented into data segments, and a data segmentof the plurality of data segments is dispersed error encoded inaccordance with dispersed error encoding parameters to produce the setof EDSs that is of pillar width. A decode threshold number of EDSs areneeded to recover the data segment, a read threshold number of EDSsprovides for reconstruction of the data segment, and a write thresholdnumber of EDSs provides for a successful transfer of the set of EDSsfrom a first at least one location in the DSN to a second at least onelocation in the DSN.

Referring to the example of operation and implementation, the DSprocessing unit (or computing device) 16 generates and maintains a SUwrite performance distribution based on monitoring of the SU-based writetransfer rates and the SU-based write failure rates. For example, the DSprocessing unit (or computing device) 16 can monitor the SUs and trackthem within a histogram (e.g., such as also described with reference toFIG. 11A) to generate the such a SU write performance distribution andfor use to compare it with one or more acceptable SU write performancedistributions and/or one or more ranges of favorably or unfavorablycomparison of the SU write performance distribution with the one or moreacceptable SU write performance distributions.

The DS processing unit (or computing device) 16 then adaptively adjustsa trimmed write threshold number of EDSs and/or a target width of EDSsfor write requests of sets of EDSs to the plurality of SUs within theDSN including the write request of the set of EDSs to the plurality ofSUs within the DSN based on favorable or unfavorable comparison of theSU write performance distribution to an acceptable SU write performancedistribution. For example, the DS processing unit (or computing device)16 may adaptively adjust (increase or decrease) the trimmed writethreshold number of EDSs based on unfavorable comparison of the SU writeperformance distribution to an acceptable SU write performancedistribution. The DS processing unit (or computing device) 16 mayadaptively adjust (increase or decrease) the target width of EDSs forwrite requests of sets of EDS based on unfavorable comparison of the SUwrite performance distribution to an acceptable SU write performancedistribution.

In some examples, the DS processing unit (or computing device) 16operates adaptively to adjust the trimmed write threshold number of EDSsto be below the write threshold number of EDSs and greater than or equalto the decode threshold number of EDSs when the SU write performancedistribution compares unfavorably to the acceptable SU write performancedistribution. The DS processing unit (or computing device) 16 alsooperates adaptively so adjust the trimmed write threshold number of EDSsto be same as the write threshold number of EDSs when the SU writeperformance distribution compares favorably to the acceptable SU writeperformance distribution.

In even other examples, the DS processing unit (or computing device) 16operates adaptively to adjust the trimmed write threshold number of EDSsto be a first number below the write threshold number of EDSs andgreater than or equal to the decode threshold number of EDSs when the SUwrite performance distribution compares unfavorably to the acceptable SUwrite performance distribution within a first unfavorable comparisonrange. The DS processing unit (or computing device) 16 also operatesadaptively to adjust the trimmed write threshold number of EDSs to be asecond number below the write threshold number of EDSs and greater thanor equal to the decode threshold number of EDSs when the SU writeperformance distribution compares unfavorably to the acceptable SU writeperformance distribution within a second unfavorable comparison range.

In other examples, the DS processing unit (or computing device) 16operates adaptively increase the target width of EDSs to be greater thanthe pillar width to specify a subset number of redundant EDSs to begenerated from the set of EDSs to be included in at least one writerequest of at least one set of EDSs to the SUs within the DSN when theSU write performance distribution compares unfavorably to the acceptableSU write performance distribution. The DS processing unit (or computingdevice) 16 also operates adaptively to adjust the target width of EDSsto be same as the pillar width when the SU write performancedistribution compares favorably to the acceptable SU write performancedistribution.

In even other examples, the DS processing unit (or computing device) 16operates adaptively increase the target width of EDSs to be greater thanthe pillar width to specify a first subset number of redundant EDSs tobe generated from the set of EDSs to be included in the at least onewrite request of the at least one set of EDSs to the SUs within the DSNwhen the SU write performance distribution compares unfavorably to theacceptable SU write performance distribution within a first unfavorablecomparison range. The DS processing unit (or computing device) 16 alsooperates adaptively to increase the target width of EDSs to be greaterthan the pillar width to specify a second subset number of redundantEDSs to be generated from the set of EDSs to be included in the at leastone write request of the at least one set of EDSs to the SUs within theDSN when the SU write performance distribution compares unfavorably tothe acceptable SU write performance distribution within a secondunfavorable comparison range.

In some other examples, the DS processing unit (or computing device) 16operates adaptively to increase the target width of EDSs to be greaterthan the pillar width to specify one or more redundant copies of the setof EDSs to be generated from the set of EDSs to be included in at leastone write request of at least one set of EDSs to the plurality of SUswithin the DSN when the SU write performance distribution comparesunfavorably to the acceptable SU write performance distribution andadaptively to adjust the target width of EDSs to be same as the pillarwidth when the SU write performance distribution compares favorably tothe acceptable SU write performance distribution.

In other examples, the DS processing unit (or computing device) 16operates to increase a time interval between write requests of sets ofEDSs to the plurality of SUs within the DSN to an extended time intervalwhen the SU write performance distribution compares unfavorably to theacceptable SU write performance distribution over a first period oftime. Then, in some instances, the DS processing unit (or computingdevice) 16 reduces the time interval between write requests of sets ofEDSs to the plurality of SUs within the DSN from the extended timeinterval to another extended time interval or the time interval when theSU write performance distribution compares favorably to the acceptableSU write performance distribution over a second period of time followingthe first period of time.

Note that the DS processing unit (or computing device) 16 may be locatedat a first premises that is remotely located from at least one SU of theSUs (e.g., storage set 910) within the DSN. Note also that the DSprocessing unit (or computing device) 16 may be any type of devicedescribed herein or their equivalent including a SU of the SUs (e.g.,storage set 910) within the DSN, a wireless smart phone, a laptop, atablet, a personal computers (PC), a work station, or a video gamedevice. Note also that the DSN may be implemented to include or be basedon any of a number of different types of communication systems includinga wireless communication system, a wire lined communication systems, anon-public intranet system, a public internet system, a local areanetwork (LAN), and/or a wide area network (WAN).

FIG. 10A is a diagram illustrating an embodiment of a method 1001 forexecution by one or more computing devices in accordance with thepresent invention. This diagram depicts another example of storing data.The method begins or continues at a step 1010 where a processing moduleof a distributed storage and task (DS) processing unit determines anumber of extra encoded data slices to store beyond a write thresholdnumber of encoded data slices of a set of encoded data slices forstorage in a set of storage units in accordance with aperformance/resource utilization approach. The performance/resourceutilization approach includes favoring performance at the expense ofresource utilization, favoring resource utilization at the expense ofperformance, and striking a balance between performance and resourceutilization. The determining includes generating the number based on oneor more of a previous value of the number of extra encoded data slices,a historical performance level of accessing encoded data slices, adesired level of performance, and a desired resource utilization level.

The method continues at a step 1020 where the processing modulefacilitate storage of the write threshold number of encoded data slicesin corresponding storage units of the set of storage units. For example,the processing module issues read slice requests to the correspondingstorage units, where the requests include the write threshold number ofencoded data slices.

The method continues at a step 1030 where the processing modulefacilitates storage of the extra number of encoded data slices incorresponding storage units of the set of storage units. For example,the processing module issues extra write slice requests to thecorresponding storage units, where the extra requests include the extranumber of encoded data slices.

FIG. 10B is a diagram illustrating another embodiment of a method 1002for execution by one or more computing devices in accordance with thepresent invention. The method 1002 operates in step 1011 by monitoringstorage unit (SU)-based write transfer rates and SU-based write failurerates associated respectively with each SU of a set storage units (SUs)(e.g., storage set) for a write request of a set of encoded data slices(EDSs) to the SUs within a dispersed storage network (DSN) via aninterface of the computing device implemented to interface andcommunicate with the DSN. Note that a data object is segmented into adata segments, and a data segment of the data segments is dispersederror encoded in accordance with dispersed error encoding parameters toproduce the set of EDSs that is of pillar width. A decode thresholdnumber of EDSs are needed to recover the data segment, a read thresholdnumber of EDSs provides for reconstruction of the data segment, and awrite threshold number of EDSs provides for a successful transfer of theset of EDSs from a first at least one location in the DSN to a second atleast one location in the DSN.

The method 1002 operates in step 1021 by generating and maintaining a SUwrite performance distribution based on monitoring of the SU-based writetransfer rates and the SU-based write failure rates and by comparing theSU write performance distribution with an acceptable SU writeperformance distribution in step 1031.

When the SU write performance distribution compares favorably with theacceptable SU write performance distribution in step 1041, the method1002 operates by first adaptively adjusting a trimmed write thresholdnumber of EDSs in step 1051. In some examples, this involves making thetrimmed write threshold number to be same as the write threshold. Insome examples, this involves making the trimmed write threshold numberto be same as the pillar width minus some desired number. If desired inoptional embodiments, the method 1002 operates in optional step 1061 byfirst adaptively adjusting a target width of EDSs for write requests ofsets of EDSs to the SUs within the DSN including the write request ofthe set of EDSs to the SUs within the DSN. In some examples, thisinvolves making target width of EDSs to be same as the pillar width.

When the SU write performance distribution compares unfavorably with theacceptable SU write performance distribution in step 1041, the method1002 operates by second adaptively adjusting the trimmed write thresholdnumber of EDSs in step 1071. In some examples, this involves making thetrimmed write threshold number to be some number below the writethreshold. If desired in optional embodiments, the method 1002 operatesin optional step 1081 by second adaptively adjusting the target width ofEDSs for write requests of sets of EDSs to the SUs within the DSNincluding the write request of the set of EDSs to the SUs within theDSN. In some examples, this involves making target width of EDSs to begreater than the pillar width.

FIG. 11A is a schematic block diagram of an example 1101 of anacceptable SU write performance distribution relative to determinedcharacteristics associated with operation of a DSN in accordance withthe present invention. A DS processing unit (or computing device)operates to monitor SUs and track their performance with respect toSU-based write transfer rates and/or SU-based write failure rates. Forexample, the DS processing unit (or computing device) operates tomonitor and track the historical operations of the SUs within the DSNand categorize the SUs within bins of a histogram based on theirperformance relative to or compared to an acceptable SU-based writetransfer rate and/or an acceptable SU-based write failure rate. Thevertical axis shows the number of SUs within each respective bin, andthe horizontal axis shows numerical values associated with SU-basedwrite transfer rates and/or SU-based write failure rates. Once such anSU write performance distribution is generated, it may be updated ormaintained (e.g., periodically after elapse of certain time periods,occasionally such as based on occurrence of certain events,continuously, etc.) so that an accurate understanding of the performanceof the SUs of a DSN may be known by a DS processing unit (or computingdevice) so that accurate decision-making may be made with respect to anypotential modification, changing, etc. of one or more parameters bywhich operation within the DSN is to be performed.

For example, such a DS processing unit (or computing device) operates togenerate and maintain a SU write performance distribution based onmonitoring of the SU-based write transfer rates and the SU-based writefailure rates. The DS processing unit (or computing device) thencompares this SU write performance distribution to one or moreacceptable SU write performance distribution (e.g., that are based onone or more an acceptable SU-based write transfer rates and/or anacceptable SU-based write failure rates) to determine how the DSN isoperating. The DS processing unit (or computing device) may then operateadaptively to adjust a trimmed write threshold number of EDSs and/or atarget width of EDSs for write requests of sets of EDSs to the SUswithin the DSN based on favorable (or unfavorable) comparison of the SUwrite performance distribution to an acceptable SU write performancedistribution.

In some examples, such a DS processing unit (or computing device)generates a histogram based on performance of the SUs based on suchconsiderations and can then generate such a SU write performancedistribution for use to compare it with one or more acceptable SU writeperformance distributions and/or one or more ranges of favorably orunfavorably comparison of the SU write performance distribution with theone or more acceptable SU write performance distributions. For example,when the SU write performance distribution compares unfavorably with anSU write performance distribution within a first unfavorable comparisonrange, the DS processing unit (or computing device) adaptively adjuststhe trimmed write threshold number of EDSs to be a first number belowthe write threshold number of EDSs and greater than or equal to thedecode threshold number of EDSs. Alternatively, when the SU writeperformance distribution compares unfavorably with the SU writeperformance distribution within a second unfavorable comparison range,the DS processing unit (or computing device) adaptively adjusts thetrimmed write threshold number of EDSs to be a second number below thewrite threshold number of EDSs and greater than or equal to the decodethreshold number of EDSs. In general, a reduction of the trimmed writethreshold number of EDSs may correspond to a reduction in the number ofEDSs that may be used to provide for a successful transfer of the set ofEDSs from a first at least one location in the DSN to a second at leastone location in the DSN. For example, instead of requiring the writethreshold number of EDSs to provide for the successful transfer of theset of EDSs from a first at least one location in the DSN to a second atleast one location in the DSN, fewer than the write threshold number ofEDSs (e.g., trimmed write threshold number of EDSs) may be used toprovide successful transfer of the set of EDSs from a first at least onelocation in the DSN to a second at least one location in the DSN. Forexample, the DS processing unit (or computing device) determines, forone or more various considerations, that fewer than the full writethreshold number of EDSs (e.g., trimmed write threshold number of EDSs)may be used to provide successful transfer of the set of EDSs from afirst at least one location in the DSN to a second at least one locationin the DSN. As an example, when the SU write performance distributioncompares unfavorably to an acceptable SU write performance distribution,then fewer than the full write threshold number of EDSs may be used andstill be deemed to provide for a successful write operation of EDSs.

Also, in some examples, when the SU write performance distributioncompares unfavorably with an SU write performance distribution within afirst unfavorable comparison range, the DS processing unit (or computingdevice) adaptively increases the target width of EDSs to be greater thanthe pillar width to specify a subset number of redundant EDSs to begenerated from the set of EDSs to be included in at least one writerequest of at least one set of EDSs to the plurality of SUs within theDSN. Alternatively, when the SU write performance distribution comparesunfavorably with the SU write performance distribution within a secondunfavorable comparison range, the DS processing unit (or computingdevice) adaptively increases the target width of EDSs to be greater thanthe pillar width. In general, an increase of the target width maycorrespond to generation of a number of redundant copies of EDSs thatmay be retrieved and used to service users of the DSN.

FIG. 11B is a schematic block diagram of an example 1102 of variousparameters associated with a set of encoded data slices (EDSs) storedwithin storage units (SUs) in accordance with the present invention.This diagram shows generally the relationship between a pillar widthnumber of SUs (and/or EDSs), a decode threshold number of SUs (and/orEDSs), a read threshold number of SUs (and/or EDSs), and a writethreshold number of SUs (and/or EDSs). When considering such numberswith respect to EDSs, note that a data object is segmented into datasegments, and a data segment of the plurality of data segments isdispersed error encoded in accordance with dispersed error encodingparameters to produce the set of EDSs that is of pillar width. A decodethreshold number of EDSs are needed to recover the data segment, a readthreshold number of EDSs provides for reconstruction of the datasegment, and a write threshold number of EDSs provides for a successfultransfer of the set of EDSs from a first at least one location in theDSN to a second at least one location in the DSN. Note also that theread threshold number and the write threshold number may be the same incertain examples and based on certain dispersed error encodingparameters. In general, the read threshold number is greater than thedecode threshold number. Also, the write threshold number is generallygreater than the read threshold number and less than the pillar width.

A trimmed write threshold EDSs and/or a target width number of EDSs maybe understood with respect to certain examples based on the variousvalues shown in this diagram. For example, a trimmed write thresholdnumber of EDSs may be viewed as allowing fewer than a full writethreshold number of EDSs to provide for a successful write operation ofEDSs. Note that different respective values of a trimmed write thresholdnumber of EDSs may be used at different times and based on differentconsiderations. In various examples, the trimmed write threshold numberof EDSs may correspond to the decode threshold number of EDSs, the readthreshold number of EDSs, and/or any other value as desired withreference to the various parameters associated with such dispersed errorencoding parameters.

In addition, a target width that is greater than the pillar width may beused to generate redundant copies of the EDSs. In one example, a targetwidth 1 corresponds to generating redundant copies of EDSs that is fewerthan the pillar width of EDSs. For example, copies of a subset of theEDSs within the pillar width number of EDSs are made for use to servicethe DSN. Alternatively, a target width 2 corresponds to generating aninteger multiple number of redundant copies of those EDSs that arewithin the pillar width of EDSs (e.g., one redundant copy of those EDSs,two redundant copies of those EDSs, three two redundant copies of thoseEDSs, etc.).

Note also that any desired values of trimmed write threshold number ofEDSs and/or target width may be adapted, modified, adjusted, etc. to beany of the various numbers as described with respect to this diagram atdifferent times and based on different considerations.

This disclosure presents various embodiments, examples, etc. that may beused to provide for scaling of the number write operations within a DSNto provide for improved performance and capability. Various parametersassociated with the dispersed error encoding parameters may be adapted,modified, etc. based on various considerations including SU-based writetransfer rates and/or the SU-based write failure rates. For example,when using write strategies that have multiple options for storagelocations (e.g., trimmed writes or target widths), there may besituations where a DS processing unit (or computing device) mayunfortunately experience an unexpectedly high latency for at least oneof the write requests, or alternatively encounter a write requestfailure.

In an example, when this happens and only a write threshold number ofEDSs are written, this may trigger either a high latency (waiting forthe slowest of the issued requests to complete) or the failure willtrigger a resistance of new write requests (thereby doubling thelatency). To prevent unexpectedly high latency events, a DS processingunit (or computing device) may operate by using a feature known as writeextra that will issue some additional number of write requests (e.g., E)in excess of the minimum write threshold. In an example, when E is 2,the DS processing unit (or computing device) can tolerate 2 failureswithout having to reissue additional write requests, as well as twounexpectedly slow DS processing units (or computing devices) withoutdecreasing latency of the operation. Note that there may be anadditional cost for such extra write operation in some DSNs (e.g., termsof network input/output (IO), storage IO, and IOPS). If desired in someexamples, to balance the tradeoffs of request latency andbandwidth/throughput, the DS processing unit (or computing device)monitors the distribution pattern of latency across the SUs (e.g., sucha SU-based write transfer rates and/or SU-based write failure rates),and may also monitor the historical error rate. When the DS processingunit (or computing device) determines there is a cost/benefit motivationto use excess bandwidth/IO/IOPS to improve or reach a latency target,the DS processing unit (or computing device) will increase the number ofextra slices (e.g., generate some redundant number of EDSs) andre-evaluate. Alternatively, if the DS processing unit (or computingdevice) determines that the motivation is in the opposite direction,then to improve bandwidth/IO/IOPS, the DS processing unit (or computingdevice) may alternatively decrease the number of extra slices (e.g.,with a lower bound for E of 0).

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A computing device comprising: an interfaceconfigured to interface and communicate with a dispersed storage network(DSN); memory that stores operational instructions; and a processingmodule operably coupled to the interface and to the memory, wherein theprocessing module, when operable within the computing device based onthe operational instructions, is configured to: monitor storage unit(SU)-based write transfer rates and SU-based write failure ratesassociated respectively with each of a plurality of storage units (SUs)for a write request of a set of encoded data slices (EDSs) to theplurality of SUs within the DSN, wherein a data object is segmented intoa plurality of data segments, wherein a data segment of the plurality ofdata segments is dispersed error encoded in accordance with dispersederror encoding parameters to produce the set of EDSs that is of pillarwidth, wherein a decode threshold number of EDSs are needed to recoverthe data segment, wherein a read threshold number of EDSs provides forreconstruction of the data segment, wherein a write threshold number ofEDSs provides for a successful transfer of the set of EDSs from a firstat least one location in the DSN to a second at least one location inthe DSN; generate and maintain a SU write performance distribution basedon monitoring of the SU-based write transfer rates and the SU-basedwrite failure rates; and adaptively adjust at least one of a trimmedwrite threshold number of EDSs or a target width of EDSs for writerequests of sets of EDSs to the plurality of SUs within the DSNincluding the write request of the set of EDSs to the plurality of SUswithin the DSN based on favorable or unfavorable comparison of the SUwrite performance distribution to an acceptable SU write performancedistribution.
 2. The computing device of claim 1, wherein the processingmodule, when operable within the computing device based on theoperational instructions, is further configured to: adaptively adjustthe trimmed write threshold number of EDSs to be below the writethreshold number of EDSs and greater than or equal to the decodethreshold number of EDSs when the SU write performance distributioncompares unfavorably to the acceptable SU write performancedistribution; and adaptively adjust the trimmed write threshold numberof EDSs to be same as the write threshold number of EDSs when the SUwrite performance distribution compares favorably to the acceptable SUwrite performance distribution.
 3. The computing device of claim 2,wherein the processing module, when operable within the computing devicebased on the operational instructions, is further configured to:adaptively adjust the trimmed write threshold number of EDSs to be afirst number below the write threshold number of EDSs and greater thanor equal to the decode threshold number of EDSs when the SU writeperformance distribution compares unfavorably to the acceptable SU writeperformance distribution within a first unfavorable comparison range;and adaptively adjust the trimmed write threshold number of EDSs to be asecond number below the write threshold number of EDSs and greater thanor equal to the decode threshold number of EDSs when the SU writeperformance distribution compares unfavorably to the acceptable SU writeperformance distribution within a second unfavorable comparison range.4. The computing device of claim 1, wherein the processing module, whenoperable within the computing device based on the operationalinstructions, is further configured to: adaptively increase the targetwidth of EDSs to be greater than the pillar width to specify a subsetnumber of redundant EDSs to be generated from the set of EDSs to beincluded in at least one write request of at least one set of EDSs tothe plurality of SUs within the DSN when the SU write performancedistribution compares unfavorably to the acceptable SU write performancedistribution; and adaptively adjust the target width of EDSs to be sameas the pillar width when the SU write performance distribution comparesfavorably to the acceptable SU write performance distribution.
 5. Thecomputing device of claim 4, wherein the processing module, whenoperable within the computing device based on the operationalinstructions, is further configured to: adaptively increase the targetwidth of EDSs to be greater than the pillar width to specify a firstsubset number of redundant EDSs to be generated from the set of EDSs tobe included in the at least one write request of the at least one set ofEDSs to the plurality of SUs within the DSN when the SU writeperformance distribution compares unfavorably to the acceptable SU writeperformance distribution within a first unfavorable comparison range;and adaptively increase the target width of EDSs to be greater than thepillar width to specify a second subset number of redundant EDSs to begenerated from the set of EDSs to be included in the at least one writerequest of the at least one set of EDSs to the plurality of SUs withinthe DSN when the SU write performance distribution compares unfavorablyto the acceptable SU write performance distribution within a secondunfavorable comparison range.
 6. The computing device of claim 1,wherein the processing module, when operable within the computing devicebased on the operational instructions, is further configured to:adaptively increase the target width of EDSs to be greater than thepillar width to specify one or more redundant copies of the set of EDSsto be generated from the set of EDSs to be included in at least onewrite request of at least one set of EDSs to the plurality of SUs withinthe DSN when the SU write performance distribution compares unfavorablyto the acceptable SU write performance distribution; and adaptivelyadjust the target width of EDSs to be same as the pillar width when theSU write performance distribution compares favorably to the acceptableSU write performance distribution.
 7. The computing device of claim 1,wherein the processing module, when operable within the computing devicebased on the operational instructions, is further configured to:increase a time interval between write requests of sets of EDSs to theplurality of SUs within the DSN to an extended time interval when the SUwrite performance distribution compares unfavorably to the acceptable SUwrite performance distribution over a first period of time; and reducethe time interval between write requests of sets of EDSs to theplurality of SUs within the DSN from the extended time interval toanother extended time interval or the time interval when the SU writeperformance distribution compares favorably to the acceptable SU writeperformance distribution over a second period of time following thefirst period of time.
 8. The computing device of claim 1, wherein thecomputing device is located at a first premises that is remotely locatedfrom at least one SU of the plurality of SUs within the DSN.
 9. Thecomputing device of claim 1 further comprising: a SU of the plurality ofSUs within the DSN, a wireless smart phone, a laptop, a tablet, apersonal computers (PC), a work station, or a video game device.
 10. Thecomputing device of claim 1, wherein the DSN includes at least one of awireless communication system, a wire lined communication systems, anon-public intranet system, a public internet system, a local areanetwork (LAN), or a wide area network (WAN).
 11. A method for executionby a computing device, the method comprising: monitoring storage unit(SU)-based write transfer rates and SU-based write failure ratesassociated respectively with each of a plurality of storage units (SUs)for a write request of a set of encoded data slices (EDSs) to theplurality of SUs within a dispersed storage network (DSN) via aninterface of the computing device implemented to interface andcommunicate with the DSN, wherein a data object is segmented into aplurality of data segments, wherein a data segment of the plurality ofdata segments is dispersed error encoded in accordance with dispersederror encoding parameters to produce the set of EDSs that is of pillarwidth, wherein a decode threshold number of EDSs are needed to recoverthe data segment, wherein a read threshold number of EDSs provides forreconstruction of the data segment, wherein a write threshold number ofEDSs provides for a successful transfer of the set of EDSs from a firstat least one location in the DSN to a second at least one location inthe DSN; generating and maintaining a SU write performance distributionbased on monitoring of the SU-based write transfer rates and theSU-based write failure rates; and adaptively adjusting at least one of atrimmed write threshold number of EDSs or a target width of EDSs forwrite requests of sets of EDSs to the plurality of SUs within the DSNincluding the write request of the set of EDSs to the plurality of SUswithin the DSN based on favorable or unfavorable comparison of the SUwrite performance distribution to an acceptable SU write performancedistribution.
 12. The method of claim 11 further comprising: adaptivelyadjusting the trimmed write threshold number of EDSs to be below thewrite threshold number of EDSs and greater than or equal to the decodethreshold number of EDSs when the SU write performance distributioncompares unfavorably to the acceptable SU write performancedistribution; and adaptively adjusting the trimmed write thresholdnumber of EDSs to be same as the write threshold number of EDSs when theSU write performance distribution compares favorably to the acceptableSU write performance distribution.
 13. The method of claim 12 furthercomprising: adaptively adjusting the trimmed write threshold number ofEDSs to be a first number below the write threshold number of EDSs andgreater than or equal to the decode threshold number of EDSs when the SUwrite performance distribution compares unfavorably to the acceptable SUwrite performance distribution within a first unfavorable comparisonrange; and adaptively adjusting the trimmed write threshold number ofEDSs to be a second number below the write threshold number of EDSs andgreater than or equal to the decode threshold number of EDSs when the SUwrite performance distribution compares unfavorably to the acceptable SUwrite performance distribution within a second unfavorable comparisonrange.
 14. The method of claim 11 further comprising: adaptivelyincreasing the target width of EDSs to be greater than the pillar widthto specify a subset number of redundant EDSs to be generated from theset of EDSs to be included in at least one write request of at least oneset of EDSs to the plurality of SUs within the DSN when the SU writeperformance distribution compares unfavorably to the acceptable SU writeperformance distribution; and adaptively adjusting the target width ofEDSs to be same as the pillar width when the SU write performancedistribution compares favorably to the acceptable SU write performancedistribution.
 15. The method of claim 14 further comprising: adaptivelyincreasing the target width of EDSs to be greater than the pillar widthto specify a first subset number of redundant EDSs to be generated fromthe set of EDSs to be included in the at least one write request of theat least one set of EDSs to the plurality of SUs within the DSN when theSU write performance distribution compares unfavorably to the acceptableSU write performance distribution within a first unfavorable comparisonrange; and adaptively increasing the target width of EDSs to be greaterthan the pillar width to specify a second subset number of redundantEDSs to be generated from the set of EDSs to be included in the at leastone write request of the at least one set of EDSs to the plurality ofSUs within the DSN when the SU write performance distribution comparesunfavorably to the acceptable SU write performance distribution within asecond unfavorable comparison range.
 16. The method of claim 11 furthercomprising: adaptively increasing the target width of EDSs to be greaterthan the pillar width to specify one or more redundant copies of the setof EDSs to be generated from the set of EDSs to be included in at leastone write request of at least one set of EDSs to the plurality of SUswithin the DSN when the SU write performance distribution comparesunfavorably to the acceptable SU write performance distribution; andadaptively adjusting the target width of EDSs to be same as the pillarwidth when the SU write performance distribution compares favorably tothe acceptable SU write performance distribution.
 17. The method ofclaim 11 further comprising: increasing a time interval between writerequests of sets of EDSs to the plurality of SUs within the DSN to anextended time interval when the SU write performance distributioncompares unfavorably to the acceptable SU write performance distributionover a first period of time; and reducing the time interval betweenwrite requests of sets of EDSs to the plurality of SUs within the DSNfrom the extended time interval to another extended time interval or thetime interval when the SU write performance distribution comparesfavorably to the acceptable SU write performance distribution over asecond period of time following the first period of time.
 18. The methodof claim 11, wherein the computing device is located at a first premisesthat is remotely located from at least one SU of the plurality of SUswithin the DSN.
 19. The method of claim 11, wherein the computing deviceis a SU of the plurality of SUs within the DSN, a wireless smart phone,a laptop, a tablet, a personal computers (PC), a work station, or avideo game device.
 20. The method of claim 11, wherein the DSN includesat least one of a wireless communication system, a wire linedcommunication systems, a non-public intranet system, a public internetsystem, a local area network (LAN), or a wide area network (WAN).